Mehranian Abolfazl, Zaidi Habib
Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland Geneva Neuroscience Centre, University of Geneva, Geneva, Switzerland; and Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, The Netherlands
J Nucl Med. 2015 Jun;56(6):877-83. doi: 10.2967/jnumed.115.154807. Epub 2015 Apr 9.
The joint maximum-likelihood reconstruction of activity and attenuation (MLAA) for emission-based attenuation correction has regained attention since the advent of time-of-flight PET/MR imaging. Recently, we improved the performance of the MLAA algorithm using an MR imaging-constrained gaussian mixture model (GMM). In this study, we compare the performance of our proposed algorithm with standard 4-class MR-based attenuation correction (MRAC) implemented on commercial systems.
Five head and neck (18)F-FDG patients were scanned on PET/MR imaging and PET/CT scanners. Dixon fat and water MR images were registered to CT images. MRAC maps were derived by segmenting the MR images into 4 tissue classes and assigning predefined attenuation coefficients. For MLAA-GMM, MR images were segmented into known tissue classes, including fat, soft tissue, lung, background air, and an unknown MR low-intensity class encompassing cortical bones, air cavities, and metal artifacts. A coregistered bone probability map was also included in the unknown tissue class. Finally, the GMM prior was constrained over known tissue classes of attenuation maps using unimodal gaussians parameterized over a patient population.
The results showed that the MLAA-GMM algorithm outperformed the MRAC method by differentiating bones from air gaps and providing more accurate patient-specific attenuation coefficients of soft tissue and lungs. It was found that the MRAC and MLAA-GMM methods resulted in average standardized uptake value errors of -5.4% and -3.5% in the lungs, -7.4% and -5.0% in soft tissues/lesions, and -18.4% and -10.2% in bones, respectively.
The proposed MLAA algorithm is promising for accurate derivation of attenuation maps on time-of-flight PET/MR systems.
自飞行时间PET/MR成像出现以来，用于基于发射的衰减校正的活动与衰减联合最大似然重建（MLAA）重新受到关注。最近，我们使用磁共振成像约束高斯混合模型（GMM）提高了MLAA算法的性能。在本研究中，我们将我们提出的算法与商业系统上实现的基于标准4类磁共振的衰减校正（MRAC）的性能进行比较。
对5例头颈部（18）F-FDG患者进行PET/MR成像和PET/CT扫描。将狄克逊脂肪和水磁共振图像配准到CT图像上。通过将磁共振图像分割为4种组织类别并指定预定义的衰减系数来生成MRAC图。对于MLAA-GMM，将磁共振图像分割为已知的组织类别，包括脂肪、软组织、肺、背景空气以及包含皮质骨、气腔和金属伪影的未知磁共振低强度类别。未知组织类别中还包括一个配准的骨概率图。最后，使用在患者群体上参数化的单峰高斯对GMM先验在衰减图的已知组织类别上进行约束。
结果表明，MLAA-GMM算法在区分骨骼和气隙以及提供更准确的患者特异性软组织和肺的衰减系数方面优于MRAC方法。发现MRAC和MLAA-GMM方法在肺中的平均标准化摄取值误差分别为-5.4%和-3.5%，在软组织/病变中为-7.4%和-5.0%，在骨骼中为-18.4%和-10.2%。
所提出的MLAA算法有望在飞行时间PET/MR系统上准确推导衰减图。
